code: def simulate_motif_counting(
      motif_name,
      seq_length,
      pos_counts,
      neg_counts,
      num_pos,
      num_neg,
      GC_fraction,
  ):
      """Generates data for motif counting task.
  
      Parameters
      ----------
      motif_name : str
      seq_length : int
      pos_counts : list
          (min_counts, max_counts) for positive set.
      neg_counts : list
          (min_counts, max_counts) for negative set.
      num_pos : int
      num_neg : int
      GC_fraction : float
  
      Returns
      -------
      sequence_arr : 1darray
          Contains sequence strings.
      y : 1darray
          Contains labels.
      embedding_arr: 1darray
          Array of embedding objects.
      """
      pos_count_sequence_array, positive_embedding_arr = motif_density(
          motif_name,
          seq_length,
          num_pos,
          pos_counts[0],
          pos_counts[1],
          GC_fraction,
      )
      neg_count_sequence_array, negative_embedding_arr = motif_density(
          motif_name,
          seq_length,
          num_pos,
          neg_counts[0],
          neg_counts[1],
          GC_fraction,
      )
      sequence_arr = np.concatenate(
          (pos_count_sequence_array, neg_count_sequence_array)
      )
      y = np.array([[True]] * num_pos + [[False]] * num_neg)
      embedding_arr = positive_embedding_arr + negative_embedding_arr
      return sequence_arr, y, embedding_arr
  
file_path: deepchem/deepchem/molnet/dnasim.py
imports: import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: simulate_motif_counting(motif_name, seq_length, pos_counts, neg_counts, num_pos, num_neg, GC_fraction) and returns: sequence_arr,  y,  embedding_arr